{"id":"https://openalex.org/W7167831132","doi":"https://doi.org/10.48550/arxiv.2607.06601","title":"TriRoute: Unified Learned Routing for Joint Adaptive Attention, Experts, and KV-Cache Allocation","display_name":"TriRoute: Unified Learned Routing for Joint Adaptive Attention, Experts, and KV-Cache Allocation","publication_year":2026,"publication_date":"2026-07-07","ids":{"openalex":"https://openalex.org/W7167831132","doi":"https://doi.org/10.48550/arxiv.2607.06601"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2607.06601","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.06601","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2607.06601","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5092911261","display_name":"Andrii Balashov","orcid":"https://orcid.org/0009-0007-5833-0888"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Balashov, Andrii","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5140372119","display_name":"Olena Ponomarova","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ponomarova, Olena","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.5205000042915344,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.5205000042915344,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.12210000306367874,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.06239999830722809,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/security-token","display_name":"Security token","score":0.5460000038146973},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.521399974822998},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.4088999927043915},{"id":"https://openalex.org/keywords/quantization","display_name":"Quantization (signal processing)","score":0.38749998807907104},{"id":"https://openalex.org/keywords/cache","display_name":"Cache","score":0.36899998784065247},{"id":"https://openalex.org/keywords/partially-observable-markov-decision-process","display_name":"Partially observable Markov decision process","score":0.34869998693466187},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.34279999136924744},{"id":"https://openalex.org/keywords/joins","display_name":"Joins","score":0.32829999923706055},{"id":"https://openalex.org/keywords/smoothing","display_name":"Smoothing","score":0.31690001487731934}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7641000151634216},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.5460000038146973},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.521399974822998},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.4088999927043915},{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.38749998807907104},{"id":"https://openalex.org/C115537543","wikidata":"https://www.wikidata.org/wiki/Q165596","display_name":"Cache","level":2,"score":0.36899998784065247},{"id":"https://openalex.org/C17098449","wikidata":"https://www.wikidata.org/wiki/Q176814","display_name":"Partially observable Markov decision process","level":4,"score":0.34869998693466187},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.34279999136924744},{"id":"https://openalex.org/C2778692605","wikidata":"https://www.wikidata.org/wiki/Q4041866","display_name":"Joins","level":2,"score":0.32829999923706055},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3260999917984009},{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.31690001487731934},{"id":"https://openalex.org/C68387754","wikidata":"https://www.wikidata.org/wiki/Q7271585","display_name":"Schedule","level":2,"score":0.31470000743865967},{"id":"https://openalex.org/C193856179","wikidata":"https://www.wikidata.org/wiki/Q5251100","display_name":"Defeasible estate","level":2,"score":0.3003000020980835},{"id":"https://openalex.org/C206729178","wikidata":"https://www.wikidata.org/wiki/Q2271896","display_name":"Scheduling (production processes)","level":2,"score":0.296999990940094},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.27720001339912415},{"id":"https://openalex.org/C101468663","wikidata":"https://www.wikidata.org/wiki/Q1620158","display_name":"Modular design","level":2,"score":0.27160000801086426},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.2712000012397766},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.2696000039577484},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.2678000032901764},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.2678000032901764},{"id":"https://openalex.org/C48105269","wikidata":"https://www.wikidata.org/wiki/Q1141160","display_name":"Header","level":2,"score":0.26089999079704285},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.25780001282691956},{"id":"https://openalex.org/C205711294","wikidata":"https://www.wikidata.org/wiki/Q176953","display_name":"Rendering (computer graphics)","level":2,"score":0.2574000060558319},{"id":"https://openalex.org/C2777472644","wikidata":"https://www.wikidata.org/wiki/Q16968992","display_name":"Approximate inference","level":3,"score":0.25699999928474426},{"id":"https://openalex.org/C203479927","wikidata":"https://www.wikidata.org/wiki/Q5165939","display_name":"Controller (irrigation)","level":2,"score":0.25690001249313354},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2556999921798706}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2607.06601","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.06601","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2607.06601","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.06601","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.461671382188797,"display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Conditional":[0],"computation":[1],"can":[2],"decouple":[3],"language":[4],"model":[5],"quality":[6],"from":[7,194],"per-token":[8],"inference":[9,212],"cost,":[10],"yet":[11],"leading":[12],"techniques":[13],"act":[14],"on":[15,173,221],"a":[16,58,80,97,106,113,120,128,145,158,163,187],"single":[17,81],"axis":[18,175],"in":[19,167],"isolation:":[20],"Mixture-of-Experts":[21],"(MoE)":[22],"sparsifies":[23],"the":[24,151,178,205,237],"FFN,":[25],"Mixture-of-Depths":[26],"(MoD)":[27],"skips":[28],"whole":[29],"transformer":[30],"blocks,":[31],"and":[32,47,53,118,138,154,180,186,214,225,242,250],"KV-cache":[33,121],"quantization":[34],"compresses":[35],"attention":[36,65,102,241],"memory.":[37],"We":[38,77,161],"argue":[39],"these":[40],"three":[41,87],"decisions":[42,137],"(attention":[43],"resolution,":[44],"expert":[45,74,115],"selection,":[46],"cache":[48,244],"bit-width)":[49],"are":[50],"strongly":[51],"coupled":[52],"should":[54],"be":[55],"made":[56],"jointly:":[57],"token":[59,92,201],"rare":[60,222,248],"enough":[61],"to":[62,177,196,245],"warrant":[63],"full":[64,240],"may":[66],"also":[67],"need":[68],"high-precision":[69,243],"caching":[70],"regardless":[71],"of":[72,109],"which":[73],"processes":[75],"it.":[76],"introduce":[78],"TriRoute,":[79],"lightweight":[82],"controller":[83,124,238],"shared":[84],"across":[85],"all":[86],"axes":[88],"that,":[89],"for":[90,135,142],"every":[91,94],"at":[93,199,210],"layer,":[95],"emits":[96],"coordinated":[98],"policy:":[99],"(i)":[100],"an":[101],"mode":[103],"(skip/local/full),":[104],"(ii)":[105],"sparse":[107],"set":[108],"FFN":[110],"experts":[111],"(with":[112],"null":[114],"recovering":[116],"MoD),":[117],"(iii)":[119],"bit-width.":[122],"The":[123],"trains":[125],"end-to-end":[126],"via":[127],"heterogeneous":[129],"relaxation":[130],"(Gumbel-Softmax":[131],"with":[132,183],"straight-through":[133],"estimation":[134],"categorical":[136],"load-balanced":[139],"top-k":[140],"gating":[141],"experts)":[143],"under":[144],"Lagrangian":[146],"budget":[147],"constraint":[148],"that":[149,227],"turns":[150],"average":[152],"compute":[153],"memory":[155],"cost":[156],"into":[157],"controllable":[159],"knob.":[160],"identify":[162],"cross-axis":[164],"routing-collapse":[165],"cascade":[166],"naive":[168],"joint":[169],"training,":[170],"where":[171],"collapse":[172],"one":[174],"propagates":[176],"others,":[179],"address":[181],"it":[182],"per-axis":[184],"normalization":[185],"coupling-aware":[188],"balancing":[189],"loss.":[190],"On":[191],"decoder-only":[192],"models":[193],"160M":[195],"1.3B":[197],"parameters":[198],"compute-optimal":[200],"counts,":[202],"TriRoute":[203],"Pareto-dominates":[204],"best":[206],"independent":[207],"MoD+MoE+KV-quantization":[208],"combination":[209],"matched":[211],"FLOPs":[213],"memory,":[215],"while":[216,253],"better":[217],"preserving":[218],"tail-case":[219],"robustness":[220],"entities,":[223,252],"code,":[224],"arithmetic":[226],"pure":[228],"perplexity":[229],"optimization":[230],"erodes.":[231],"Post-hoc":[232],"analysis":[233],"reveals":[234],"interpretable":[235],"structure:":[236],"allocates":[239],"sentence-initial":[246],"positions,":[247],"subwords,":[249],"named":[251],"cheaply":[254],"routing":[255],"function":[256],"words.":[257]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-07-10T00:00:00"}